作为临床决策支持系统使用的大型语言模型的测量性能和医疗保健专业感知:一个范围审查
Solène Delourme1,2, Akram Redjdal1, Jacques Bouaud1
1Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Paris, France.
Studies in health technology and informatics
|August 23, 2024
概括
大型语言模型 (LLM) 显示出作为瘤学临床决策支持系统 (CDSS) 的承诺,解决癌症护理方面的挑战. 需要进一步的研究来弥合用户感知和这些AI工具的实际性能之间的差距.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 医疗保健面临着瘤董事会效率和护理质量的挑战.
- 临床决策支持系统 (CDSS) 在实践中未得到充分利用.
- 像ChatGPT这样的大型语言模型 (LLM) 提供了改善临床支持的潜力.
研究的目的:
- 评估LLM作为CDSS在瘤学和其他专业中的实用性.
- 将用户对LLM的看法与其测量性能进行比较.
- 探索癌症护理挑战的创新解决方案.
主要方法:
- 覆盖范围审查方法.
- 评估LLMs (例如,ChatGPT) 作为CDSS.
- 用户感知与系统性能数据的比较.
主要成果:
- 在支持癌症诊断和治疗规划方面,LLM显示出潜在的实用性.
- 在临床医生的看法和LLMs的测量表现之间存在差异.
- 目前的LLM整合到临床工作流程中需要进一步调查.
结论:
- 临床决策支持 (LLM) 是提高瘤学临床决策支持的有希望的途径.
- 解决认知和实际LLM绩效之间的差距对于有效实施至关重要.
- 进一步的研究应该集中在优化LLM整合和验证它们的临床实用性.
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